Model averaging in semiparametric estimation of treatment effects

Toru Kitagawa () and Chris Muris
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Toru Kitagawa: Institute for Fiscal Studies and University College London

No CWP46/15, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: In the practice of program evaluation, choosing the covariates and the functional form of the propensity score is an important choice that the researchers make when estimating treatment e?ects. This paper proposes a data-driven way of averaging the estimators over the candidate speci?cations in order to resolve the issue of speci?cation uncertainty in the propensity score weighting estimation of the average treatment e?ects for treated (ATT). The proposed averaging procedures aim to minimize the estimated mean squared error (MSE) of the ATT estimator in a local asymptotic framework. We formulate model averaging as a statistical decision problem in a limit experiment, and derive an averaging scheme that is Bayes optimal with respect to a given prior for the localization parameters. Analytical comparisons of the Bayes asymptotic MSE show that the averaging estimator outperforms post model selection estimators and the estimators in any of the candidate models. Our Monte Carlo studies con?rm these theoretical results and illustrate the size of the MSE gains from averaging. We apply the averaging procedure to evaluate the e?ect of the labor market program analyzed in LaLonde (1986).

Date: 2015-08-13
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Model averaging in semiparametric estimation of treatment effects (2016)
Working Paper: Model averaging in semiparametric estimation of treatment effects (2015)
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